├── README.md
├── added_tokens.json
├── config.json
├── generation_config.json
├── special_tokens_map.json
├── spiece.model
└── tokenizer_config.json


/README.md:
--------------------------------------------------------------------------------
 1 | # Stock Market QA Chatbot with Text-to-Text Transfer Transformer(T5)
 2 | 
 3 | ## 📌 Overview
 4 | 
 5 | This repository hosts the quantized version of the T5 model fine-tuned for question-answer tasks related to stock market. The model has been trained on the stock_trading_QA dataset from Hugging Face. The model is quantized to Float16 (FP16) to optimize inference speed and efficiency while maintaining high performance.
 6 | 
 7 | ## 🏗 Model Details
 8 | 
 9 | - **Model Architecture:** t5-base
10 | - **Task:** QA Chatbot for Stock Market  
11 | - **Dataset:** Hugging Face's `stock_trading_QA`  
12 | - **Quantization:** Float16 (FP16) for optimized inference  
13 | - **Fine-tuning Framework:** Hugging Face Transformers  
14 | 
15 | ## 🚀 Usage
16 | 
17 | ### Installation
18 | 
19 | ```bash
20 | pip install transformers torch
21 | ```
22 | 
23 | ### Loading the Model
24 | 
25 | ```python
26 | from transformers import T5Tokenizer, T5ForConditionalGeneration
27 | import torch
28 | 
29 | device = "cuda" if torch.cuda.is_available() else "cpu"
30 | 
31 | model_name = "AventIQ-AI/t5-stockmarket-qa-chatbot"
32 | model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
33 | tokenizer = T5Tokenizer.from_pretrained(model_name)
34 | ```
35 | 
36 | ### Question Answer Example
37 | 
38 | ```python
39 | question = "How can I start investing in stocks?"
40 | input_text = "question: " + question
41 | input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
42 | 
43 | with torch.no_grad():
44 |     outputs = model.generate(input_ids, max_length=50)
45 |     answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
46 | 
47 | print(f"Question: {question}")
48 | print(f"Predicted Answer: {answer}")
49 | ```
50 | 
51 | ## 📊 Evaluation Metric: BLEU Score
52 | 
53 | For question answer tasks, a high BLEU score indicates that the model’s corrected sentences closely match human-annotated corrections.  
54 | 
55 | ## **Interpreting Our BLEU Score**  
56 | Our model achieved a **BLEU score of 0.7888**, which indicates:  
57 | ✅ **Good answer generating ability**  
58 | ✅ **Moderate sentence fluency**    
59 | 
60 | BLEU is computed by comparing the **1-gram, 2-gram, 3-gram, and 4-gram overlaps** between the model’s output and the reference sentence while applying a **brevity penalty** if the model generates shorter sentences.  
61 | 
62 | ### **BLEU Score Ranges for Chatbot**
63 | 
64 | | BLEU Score | Interpretation |
65 | | --- | --- |
66 | | **0.8 - 1.0** | Near-perfect corrections, closely matching human annotations. |
67 | | **0.7 - 0.8** | High-quality corrections, minor variations in phrasing. |
68 | | **0.6 - 0.7** | Good corrections, but with some grammatical errors or missing words. |
69 | | **0.5 - 0.6** | Decent corrections, noticeable mistakes, lacks fluency. |
70 | | **Below 0.5** | Needs improvement, frequent incorrect corrections. |
71 | 
72 | 
73 | ## ⚡ Quantization Details
74 | 
75 | Post-training quantization was applied using PyTorch's built-in quantization framework. The model was quantized to Float16 (FP16) to reduce model size and improve inference efficiency while balancing accuracy.
76 | 
77 | ## 📂 Repository Structure
78 | 
79 | ```
80 | .
81 | ├── model/               # Contains the quantized model files
82 | ├── tokenizer_config/    # Tokenizer configuration and vocabulary files
83 | ├── model.safetensors/   # Quantized Model
84 | ├── README.md            # Model documentation
85 | ```
86 | 
87 | ## ⚠️ Limitations
88 | 
89 | - The model may struggle with highly ambiguous sentences.
90 | - Quantization may lead to slight degradation in accuracy compared to full-precision models.
91 | - Performance may vary across different writing styles and sentence structures.
92 | 
93 | ## 🤝 Contributing
94 | 
95 | Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
96 | 


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/added_tokens.json:
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/config.json:
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 1 | {
 2 |   "_name_or_path": "/kaggle/working/qa-stock-model",
 3 |   "architectures": [
 4 |     "T5ForConditionalGeneration"
 5 |   ],
 6 |   "classifier_dropout": 0.0,
 7 |   "d_ff": 3072,
 8 |   "d_kv": 64,
 9 |   "d_model": 768,
10 |   "decoder_start_token_id": 0,
11 |   "dense_act_fn": "relu",
12 |   "dropout_rate": 0.1,
13 |   "eos_token_id": 1,
14 |   "feed_forward_proj": "relu",
15 |   "initializer_factor": 1.0,
16 |   "is_encoder_decoder": true,
17 |   "is_gated_act": false,
18 |   "layer_norm_epsilon": 1e-06,
19 |   "model_type": "t5",
20 |   "n_positions": 512,
21 |   "num_decoder_layers": 12,
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23 |   "num_layers": 12,
24 |   "output_past": true,
25 |   "pad_token_id": 0,
26 |   "relative_attention_max_distance": 128,
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28 |   "task_specific_params": {
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40 |       "max_length": 300,
41 |       "num_beams": 4,
42 |       "prefix": "translate English to German: "
43 |     },
44 |     "translation_en_to_fr": {
45 |       "early_stopping": true,
46 |       "max_length": 300,
47 |       "num_beams": 4,
48 |       "prefix": "translate English to French: "
49 |     },
50 |     "translation_en_to_ro": {
51 |       "early_stopping": true,
52 |       "max_length": 300,
53 |       "num_beams": 4,
54 |       "prefix": "translate English to Romanian: "
55 |     }
56 |   },
57 |   "torch_dtype": "float16",
58 |   "transformers_version": "4.47.0",
59 |   "use_cache": true,
60 |   "vocab_size": 32128
61 | }
62 | 


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/generation_config.json:
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1 | {
2 |   "_from_model_config": true,
3 |   "decoder_start_token_id": 0,
4 |   "eos_token_id": 1,
5 |   "pad_token_id": 0,
6 |   "transformers_version": "4.47.0"
7 | }
8 | 


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/special_tokens_map.json:
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  1 | {
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/spiece.model:
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https://raw.githubusercontent.com/Avent-IQ/t5-stockmarket-qa-chatbot/e0f2a40ab927c530ec3fbe761c50926b883d2441/spiece.model


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/tokenizer_config.json:
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833 |     "<extra_id_3>",
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840 |     "<extra_id_10>",
841 |     "<extra_id_11>",
842 |     "<extra_id_12>",
843 |     "<extra_id_13>",
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846 |     "<extra_id_16>",
847 |     "<extra_id_17>",
848 |     "<extra_id_18>",
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850 |     "<extra_id_20>",
851 |     "<extra_id_21>",
852 |     "<extra_id_22>",
853 |     "<extra_id_23>",
854 |     "<extra_id_24>",
855 |     "<extra_id_25>",
856 |     "<extra_id_26>",
857 |     "<extra_id_27>",
858 |     "<extra_id_28>",
859 |     "<extra_id_29>",
860 |     "<extra_id_30>",
861 |     "<extra_id_31>",
862 |     "<extra_id_32>",
863 |     "<extra_id_33>",
864 |     "<extra_id_34>",
865 |     "<extra_id_35>",
866 |     "<extra_id_36>",
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868 |     "<extra_id_38>",
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870 |     "<extra_id_40>",
871 |     "<extra_id_41>",
872 |     "<extra_id_42>",
873 |     "<extra_id_43>",
874 |     "<extra_id_44>",
875 |     "<extra_id_45>",
876 |     "<extra_id_46>",
877 |     "<extra_id_47>",
878 |     "<extra_id_48>",
879 |     "<extra_id_49>",
880 |     "<extra_id_50>",
881 |     "<extra_id_51>",
882 |     "<extra_id_52>",
883 |     "<extra_id_53>",
884 |     "<extra_id_54>",
885 |     "<extra_id_55>",
886 |     "<extra_id_56>",
887 |     "<extra_id_57>",
888 |     "<extra_id_58>",
889 |     "<extra_id_59>",
890 |     "<extra_id_60>",
891 |     "<extra_id_61>",
892 |     "<extra_id_62>",
893 |     "<extra_id_63>",
894 |     "<extra_id_64>",
895 |     "<extra_id_65>",
896 |     "<extra_id_66>",
897 |     "<extra_id_67>",
898 |     "<extra_id_68>",
899 |     "<extra_id_69>",
900 |     "<extra_id_70>",
901 |     "<extra_id_71>",
902 |     "<extra_id_72>",
903 |     "<extra_id_73>",
904 |     "<extra_id_74>",
905 |     "<extra_id_75>",
906 |     "<extra_id_76>",
907 |     "<extra_id_77>",
908 |     "<extra_id_78>",
909 |     "<extra_id_79>",
910 |     "<extra_id_80>",
911 |     "<extra_id_81>",
912 |     "<extra_id_82>",
913 |     "<extra_id_83>",
914 |     "<extra_id_84>",
915 |     "<extra_id_85>",
916 |     "<extra_id_86>",
917 |     "<extra_id_87>",
918 |     "<extra_id_88>",
919 |     "<extra_id_89>",
920 |     "<extra_id_90>",
921 |     "<extra_id_91>",
922 |     "<extra_id_92>",
923 |     "<extra_id_93>",
924 |     "<extra_id_94>",
925 |     "<extra_id_95>",
926 |     "<extra_id_96>",
927 |     "<extra_id_97>",
928 |     "<extra_id_98>",
929 |     "<extra_id_99>"
930 |   ],
931 |   "clean_up_tokenization_spaces": false,
932 |   "eos_token": "</s>",
933 |   "extra_ids": 100,
934 |   "extra_special_tokens": {},
935 |   "legacy": true,
936 |   "model_max_length": 1000000000000000019884624838656,
937 |   "pad_token": "<pad>",
938 |   "sp_model_kwargs": {},
939 |   "tokenizer_class": "T5Tokenizer",
940 |   "unk_token": "<unk>"
941 | }
942 | 


--------------------------------------------------------------------------------